Yet Another Automated Gleason Grading System (YAAGGS) by weakly supervised deep learning.

ABSTRACT: The Gleason score contributes significantly in predicting prostate cancer outcomes and selecting the appropriate treatment option, which is affected by well-known inter-observer variations. We present a novel deep learning-based automated Gleason grading system that does not require extensive region-level manual annotations by experts and/or complex algorithms for the automatic generation of region-level annotations. A total of 6664 and 936 prostate needle biopsy single-core slides (689 and 99 cases) from two institutions were used for system discovery and validation, respectively. Pathological diagnoses were converted into grade groups and used as the reference standard. The grade group prediction accuracy of the system was 77.5% (95% confidence interval (CI): 72.3-82.7%), the Cohen's kap

SUBMITTER: Mun Y 

PROVIDER: S-EPMC8203612 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature